89,724 results match your criteria neural network

Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks.

Nanotechnology 2021 May 10. Epub 2021 May 10.

Wuhan National Lab for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, CHINA.

The development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. How to efficiently design these devices is an active area of research. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. Read More

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Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity.

Med Image Anal 2021 Apr 21;71:102084. Epub 2021 Apr 21.

BASIRA lab, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.

Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N nodes (i. Read More

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Learning-based defect recognition for quasi-periodic HRSTEM images.

Micron 2021 May 3;146:103069. Epub 2021 May 3.

IBM Research Europe - Zurich, Rüschlikon, 8803, Switzerland. Electronic address:

Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect analysis on the sub-nanometer scale is enabled by high-resolution scanning transmission electron microscopy (HRSTEM), where the identification of defects is currently carried out based on human expertise. However, the process is tedious, highly time consuming and, in some cases, yields ambiguous results. Read More

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Gonadal steroid hormone receptors in the medial amygdala contribute to experience-dependent changes in stress vulnerability.

Psychoneuroendocrinology 2021 May 3;129:105249. Epub 2021 May 3.

Department of Psychology, University of Tennessee, Knoxville, TN 37996, United States.

Social experience can generate neural plasticity that changes how individuals respond to stress. Winning aggressive encounters alters how animals respond to future challenges and leads to increased plasma testosterone concentrations and androgen receptor (AR) expression in the social behavior neural network. In this project, our aim was to identify neuroendocrine mechanisms that account for changes in stress-related behavior following the establishment of dominance relationships over a two-week period. Read More

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Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

Comput Biol Med 2021 Apr 29;134:104425. Epub 2021 Apr 29.

Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom. Electronic address:

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. Read More

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Benzophenone-3 induced abnormal development of enteric nervous system in zebrafish through MAPK/ERK signaling pathway.

Chemosphere 2021 May 5;280:130670. Epub 2021 May 5.

Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. Electronic address:

Hirschsprung disease (HSCR) is a congenital disease characterized by the absence of enteric neurons, which is derived from the failure of the proliferation, differentiation or migration of the enteric neural crest cells (ENCCs). HSCR is associated with multiple risk factors, including polygenic inheritance factors and environmental factors. Genetic studies have been extensively performed, whereas studies related to environmental factors remain insufficient. Read More

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Large-scale network topology reveals brain functional abnormality in Chinese dyslexic children.

Neuropsychologia 2021 May 7:107886. Epub 2021 May 7.

State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, PR China. Electronic address:

It has been revealed that dyslexic children learning alphabetic languages are characterized by aberrant topological organization of brain networks. However, little is known about the functional organization and the reconfiguration pattern of brain networks in Chinese dyslexic children. Using graph theoretical analysis and functional magnetic resonance images (fMRI), we examined this issue specifically from the perspective of functional integration and segregation. Read More

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Accurate prediction of protein-ATP binding residues using position-specific frequency matrix.

Anal Biochem 2021 May 7:114241. Epub 2021 May 7.

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, 310023. Electronic address:

Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially for the case where only protein sequence information is given. In this study, we propose a novel method, named DeepATPseq, to predict protein-ATP binding residues without using any information about protein three-dimension structure or sequence-derived structural information. Read More

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Mixed-precision weights network for field-programmable gate array.

PLoS One 2021 10;16(5):e0251329. Epub 2021 May 10.

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan.

In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {-1,1}, ternary {-1,0,1}, and 32-bit floating-point. We further developed the MPWN from both software and hardware aspects. From the software aspect, we evaluated the MPWN on the Fashion-MNIST and CIFAR10 datasets. Read More

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Predicting mean ribosome load for 5'UTR of any length using deep learning.

PLoS Comput Biol 2021 May 10;17(5):e1008982. Epub 2021 May 10.

Department of Informatics, Technical University of Munich, Garching, Germany.

The 5' untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5'UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)-a proxy for translation rate-directly from 5'UTR sequence with a high degree of accuracy. Read More

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Detection of Emotional Sensitivity Using fNIRS based Dynamic Functional Connectivity.

IEEE Trans Neural Syst Rehabil Eng 2021 May 10;PP. Epub 2021 May 10.

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k-means clustering technique was applied to derive four recurring connectivity states. Read More

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What Executive Function Network is that? An Image-Based Meta-Analysis of Network Labels.

Brain Topogr 2021 May 10. Epub 2021 May 10.

Department of Physics, Florida International University, Miami, FL, 33199, USA.

The current state of label conventions used to describe brain networks related to executive functions is highly inconsistent, leading to confusion among researchers regarding network labels. Visually similar networks are referred to by different labels, yet these same labels are used to distinguish networks within studies. We performed a literature review of fMRI studies and identified nine frequently-used labels that are used to describe topographically or functionally similar neural networks: central executive network (CEN), cognitive control network (CCN), dorsal attention network (DAN), executive control network (ECN), executive network (EN), frontoparietal network (FPN), working memory network (WMN), task positive network (TPN), and ventral attention network (VAN). Read More

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Biallelic variants in HPDL cause pure and complicated hereditary spastic paraplegia.

Brain 2021 May 10. Epub 2021 May 10.

Genetics Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.

Human 4-hydroxyphenylpyruvate dioxygenase-like (HPDL) is a putative iron-containing non-heme oxygenase of unknown specificity and biological significance. We report 25 families containing 34 individuals with neurological disease associated with biallelic HPDL variants. Phenotypes ranged from juvenile-onset pure hereditary spastic paraplegia to infantile-onset spasticity and global developmental delays, sometimes complicated by episodes of neurological and respiratory decompensation. Read More

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Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation.

JMIR Med Inform 2021 May 10;9(5):e25304. Epub 2021 May 10.

The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Background: Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. Read More

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A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power.

Int J Neural Syst 2021 May 7:2150022. Epub 2021 May 7.

Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China.

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. Read More

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The Working Memory Network and Its Association with Working Memory Performance in Survivors of non-CNS Childhood Cancer.

Dev Neuropsychol 2021 Apr-Jun;46(3):249-264

Division of Neuropediatrics, Development and Rehabilitation, Children's University Hospital, Inselspital, University of Bern, Bern, Switzerland.

Childhood cancer and its treatment puts survivors at risk of low working memory capacity. Working memory represents a core cognitive function, which is crucial in daily life and academic tasks. The aim of this functional MRI (fMRI) study was to examine the working memory network of survivors of childhood cancer without central nervous system (CNS) involvement and its relation to cognitive performance. Read More

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Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

Technol Cancer Res Treat 2021 Jan-Dec;20:15330338211016373

Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.

Background: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning. Read More

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Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

Radiol Cardiothorac Imaging 2021 Apr 8;3(2):e200477. Epub 2021 Apr 8.

Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.).

Purpose: To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.

Materials And Methods: In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). Read More

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A Novel Machine Learning Model for Dose Prediction in Prostate Volumetric Modulated Arc Therapy Using Output Initialization and Optimization Priorities.

Front Artif Intell 2021 23;4:624038. Epub 2021 Apr 23.

Department of Radiation Oncology, Duke Cancer Institute, Durham, NC, United States.

Treatment planning for prostate volumetric modulated arc therapy (VMAT) can take 5-30 min per plan to optimize and calculate, limiting the number of plan options that can be explored before the final plan decision. Inspired by the speed and accuracy of modern machine learning models, such as residual networks, we hypothesized that it was possible to use a machine learning model to bypass the time-intensive dose optimization and dose calculation steps, arriving directly at an estimate of the resulting dose distribution for use in multi-criteria optimization (MCO). In this study, we present a novel machine learning model for predicting the dose distribution for a given patient with a given set of optimization priorities. Read More

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Application of Artificial Neural Network to Preoperative F-FDG PET/CT for Predicting Pathological Nodal Involvement in Non-small-cell Lung Cancer Patients.

Front Med (Lausanne) 2021 22;8:664529. Epub 2021 Apr 22.

Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.

To evaluate the performance of artificial neural networks (aNN) applied to preoperative F-FDG PET/CT for predicting nodal involvement in non-small-cell lung cancer (NSCLC) patients. We retrospectively analyzed data from 540 clinically resectable NSCLC patients (333 M; 67.4 ± 9 years) undergone preoperative F-FDG PET/CT and pulmonary resection with hilo-mediastinal lymphadenectomy. Read More

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Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature.

Front Oncol 2021 22;11:585942. Epub 2021 Apr 22.

Engineering Research Center of Molecular & Neuro-imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.

Background And Purpose: The preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.

Materials And Methods: This retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Read More

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Exploring safe and potent bioactives for the treatment of non-small cell lung cancer.

3 Biotech 2021 May 26;11(5):241. Epub 2021 Apr 26.

Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014 India.

Activating and suppressing mutations in the MAPK pathway receptors are the primary causes of NSCLC. Of note, MEK inhibition is considered a promising strategy because of the diverse structures and harmful effects of upstream receptors in MAPK pathway. Thus, we explore a total of 1574 plant-based bioactive compounds activity against MEK using an energy-based virtual screening strategy. Read More

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Acceleration of PDE-Based Biological Simulation Through the Development of Neural Network Metamodels.

Curr Pathobiol Rep 2020 Dec 6;8(4):121-131. Epub 2020 Nov 6.

Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907.

Purpose Of Review: Partial differential equation (PDE) mathematical models of biological systems and the simulation approaches used to solve them are widely used to test hypotheses and infer regulatory interactions based on optimization of the PDE model against the observed data. In this review, we discuss the ability of powerful machine learning methods to accelerate the parametric screening of biophysical informed- PDE systems.

Recent Findings: A major shortcoming in more broad adaptation of PDE-based models is the high computational complexity required to solve and optimize the models and it requires many simulations to traverse the very high-dimensional parameter spaces during model calibration and inference tasks. Read More

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December 2020

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area.

Scientifica (Cairo) 2021 20;2021:8810279. Epub 2021 Apr 20.

Faculty of Science Semlalia, Cadi Ayyad University, Marrakesh 40000, Morocco.

The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc. Read More

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Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data.

Health Inf Sci Syst 2021 Dec 4;9(1):20. Epub 2021 May 4.

Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245-Porto Alegre, Rio Grande do Sul, Brazil.

Introduction: Hepatocellular carcinoma is the prevalent primary liver cancer, a silent disease that killed 782,000 worldwide in 2018. Multimodal deep learning is the application of deep learning techniques, fusing more than one data modality as the model's input.

Purpose: A computer-aided diagnosis system for hepatocellular carcinoma developed with multimodal deep learning approaches could use multiple data modalities as recommended by clinical guidelines, and enhance the robustness and the value of the second-opinion given to physicians. Read More

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December 2021

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain.

J Healthc Eng 2021 21;2021:9958017. Epub 2021 Apr 21.

Anhui Key Laboratory of Plant Resources and Plant Biology, Huaibei Normal University, Huaibei 235000, China.

The traditional medical image fusion methods, such as the famous multi-scale decomposition-based methods, usually suffer from the bad sparse representations of the salient features and the low ability of the fusion rules to transfer the captured feature information. In order to deal with this problem, a medical image fusion method based on the scale invariant feature transformation (SIFT) descriptor and the deep convolutional neural network (CNN) in the shift-invariant shearlet transform (SIST) domain is proposed. Firstly, the images to be fused are decomposed into the high-pass and the low-pass coefficients. Read More

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Pneumonia Detection Using an Improved Algorithm Based on Faster R-CNN.

Comput Math Methods Med 2021 21;2021:8854892. Epub 2021 Apr 21.

Institute of Advanced Digital Technology and Instrumentation, Zhejiang University and State Key Laboratory of Industrial Control Technology, Zhejiang University, Zhejiang 310027, China.

Pneumonia remains a threat to human health; the coronavirus disease 2019 (COVID-19) that began at the end of 2019 had a major impact on the world. It is still raging in many countries and has caused great losses to people's lives and property. In this paper, we present a method based on DeepConv-DilatedNet of identifying and localizing pneumonia in chest X-ray (CXR) images. Read More

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Intravenous Delayed Gadolinium-Enhanced MR Imaging of the Endolymphatic Space: A Methodological Comparative Study.

Front Neurol 2021 22;12:647296. Epub 2021 Apr 22.

Department of Neurology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.

non-invasive verification of endolymphatic hydrops (ELH) by means of intravenous delayed gadolinium (Gd) enhanced magnetic resonance imaging of the inner ear (iMRI) is rapidly developing into a standard clinical tool to investigate peripheral vestibulo-cochlear syndromes. In this context, methodological comparative studies providing standardization and comparability between labs seem even more important, but so far very few are available. One hundred eight participants [75 patients with Meniere's disease (MD; 55. Read More

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Computer Vision for Brain Disorders Based Primarily on Ocular Responses.

Front Neurol 2021 21;12:584270. Epub 2021 Apr 21.

Guangdong-Hong Kong-Macau Institute of Central Nervous System (CNS) Regeneration, Jinan University, Guangzhou, China.

Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and spontaneous blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. In this review, we outline multiple techniques in which ocular assessments may serve as a non-invasive approach for the early detections of various brain disorders, such as autism spectrum disorder (ASD), Alzheimer's disease (AD), schizophrenia (SZ), and major depressive disorder (MDD). Read More

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Aberrant Resting-State Cerebellar-Cerebral Functional Connectivity in Unmedicated Patients With Obsessive-Compulsive Disorder.

Front Psychiatry 2021 23;12:659616. Epub 2021 Apr 23.

Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.

Although abnormality of cerebellar-cerebral functional connectivity at rest in obsessive-compulsive disorder (OCD) has been hypothesized, only a few studies have investigated the neural mechanism. To verify the findings of previous studies, a large sample of patients with OCD was studied because OCD shows possible heterogeneity. Forty-seven medication-free patients with OCD and 62 healthy controls (HCs) underwent resting-state functional magnetic imaging scans. Read More

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